Dataset Information

Comprehensive Proteomic Characterization of the Human Colorectal Carcinoma Reveals Signature Proteins and Perturbed Pathways.

ABSTRACT: The global change in protein abundance in colorectal cancer (CRC) and its contribution to tumorigenesis have not been comprehensively analyzed. In this study, we conducted a comprehensive proteomic analysis of paired tumors and adjacent tissues (AT) using high-resolution Fourier-transform mass spectrometry and a novel algorithm of quantitative pathway analysis. 12380 proteins were identified and 740 proteins that presented a 4-fold change were considered a CRC proteomic signature. A significant pattern of changes in protein abundance was uncovered which consisted of an imbalance in protein abundance of inhibitory and activating regulators in key signal pathways, a significant elevation of proteins in chromatin modification, gene expression and DNA replication and damage repair, and a decreased expression of proteins responsible for core extracellular matrix architectures. Specifically, based on the relative abundance, we identified a panel of 11 proteins to distinguish CRC from AT. The protein that showed the greatest degree of overexpression in CRC compared to AT was Dipeptidase 1 (DPEP1). Knockdown of DPEP1 in SW480 and HCT116 cells significantly increased cell apoptosis and attenuated cell proliferation and invasion. Together, our results show one of largest dataset in CRC proteomic research and provide a molecular link from genomic abnormalities to the tumor phenotype.

SUBMITTER: Hao JJ

PROVIDER: S-EPMC5299448 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Comprehensive Proteomic Characterization of the Human Colorectal Carcinoma Reveals Signature Proteins and Perturbed Pathways.

Hao Jian-Jiang JJ Zhi Xiaofei X Wang Yeming Y Zhang Zheng Z Hao Zeyu Z Ye Rong R Tang Zhijie Z Qian Fei F Wang Quhui Q Zhu Jianwei J

Scientific reports 20170209

The global change in protein abundance in colorectal cancer (CRC) and its contribution to tumorigenesis have not been comprehensively analyzed. In this study, we conducted a comprehensive proteomic analysis of paired tumors and adjacent tissues (AT) using high-resolution Fourier-transform mass spectrometry and a novel algorithm of quantitative pathway analysis. 12380 proteins were identified and 740 proteins that presented a 4-fold change were considered a CRC proteomic signature. A significant ...[more]

PMID: 28181595

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Project description:BackgroundDue to the complicated molecular and cellular heterogeneity in hepatocellular carcinoma (HCC), the morbidity and mortality still remains high level in the world. However, the number of novel metabolic biomarkers and prognostic models could be applied to predict the survival of HCC patients is still small. In this study, we constructed a metabolic gene signature by systematically analyzing the data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC).MethodsDifferentially expressed genes (DEGs) between tumors and paired non-tumor samples of 50 patients from TCGA dataset were calculated for subsequent analysis. Univariate cox proportional hazard regression and LASSO analysis were performed to construct a gene signature. The Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC), Univariate and Multivariate Cox regression analysis, stratification analysis were used to assess the prognostic value of the gene signature. Furthermore, the reliability and validity were validated in four types of testing cohorts. Moreover, the diagnostic capability of the gene signature was investigated to further explore the clinical significance. Finally, Go enrichment analysis and Gene Set Enrichment Analysis (GSEA) have been performed to reveal the different biological processes and signaling pathways which were active in high risk or low risk group.ResultsTen prognostic genes were identified and a gene signature were constructed to predict overall survival (OS). The gene signature has demonstrated an excellent ability for predicting survival prognosis. Univariate and Multivariate analysis revealed the gene signature was an independent prognostic factor. Furthermore, stratification analysis indicated the model was a clinically and statistically significant for all subgroups. Moreover, the gene signature demonstrated a high diagnostic capability in differentiating normal tissue and HCC. Finally, several significant biological processes and pathways have been identified to provide new insights into the development of HCC.ConclusionThe study have identified ten metabolic prognostic genes and developed a prognostic gene signature to provide more powerful prognostic information and improve the survival prediction for HCC.

Project description:BackgroundCurrently, the most predominant type of liver cancer is hepatocellular carcinoma (HCC), which is also the fourth leading cause of cancer-related death in the global population. Pyroptosis is an emerging form of cell death that affects the prognosis of cancer patients by modulating tumor cell migration, proliferation and invasion. However, the evaluation of pyroptosis in the prognosis of HCC is still insufficient.MethodsA total of 365 HCC patients from the TCGA-LIHC cohort were classified into two distinct subtypes using consensus clustering of pyroptosis-related genes (PRGs). Following univariate Cox analysis of differentially expressed genes between subtypes, we established a prognostic model (PRGs-score, PRGS) by LASSO Cox analysis. We further tested the predictive power of the prognostic model in the ICGC (LIRI-JP) and GEO (GSE14520) cohorts. The tumor microenvironment (TME) was studied using the CIBERSORT. The enrichment scores for immune cells and immune functions in low- and high-PRGS groups were assessed using ssGSEA. The IMvigor210 cohort was used to investigate the immunotherapy efficacy. Furthermore, we validated the expression of prognostic genes in PRGS by RT-qPCR in vitro.ResultsThe subtyping of HCC based on PRGs exhibited distinct clinical characteristics. We developed a prognostic model PRGS by differentially expressed genes between different subtypes. The results showed that PRGS could well forecast the survival of HCC patients in different cohorts and was associated with the immune microenvironment. Moreover, PRGS was considered to be an independent prognostic risk factor and superior to other pyroptosis-related signatures. Low-PRGS implied greater immune cell infiltration and better overall survival with immunotherapy. The results of RT-qPCR also showed that prognostic genes were significantly dysregulated in HCC.ConclusionsPRGS has promising application in forecasting the prognosis of HCC patients, and its relationship with the immune microenvironment provides a basis for the subsequent treatment and research of HCC.

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Dataset Information

Comprehensive Proteomic Characterization of the Human Colorectal Carcinoma Reveals Signature Proteins and Perturbed Pathways.

Publications

Comprehensive Proteomic Characterization of the Human Colorectal Carcinoma Reveals Signature Proteins and Perturbed Pathways.

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